针对在动态环境中,视觉定位系统的定位精度和鲁棒性容易受到动态特征点影响的问题,提出了一种联合目标检测与深度信息的动态特征点去除方法.引入YOLOv7目标检测网络快速获得当前图像帧的目标类别及位置信息,加入坐标注意力(coordinate attention,CA)机制优化深度学习模型,提升网络目标检测精度.此外,提出了 一种利用深度信息和对极几何约束的动态特征点优化策略.有效剔除了动态特征点,同时保留了尽量多的静态点,从而降低了动态点对系统定位精度和鲁棒性的影响.在公开的数据集TUM上进行实验验证.结果表明:与ORB-SLAM2(oriented fast and rotated brief-SLAM)相比,所提方案在定位精度和鲁棒性上有明显优势.同时与动态同步定位和地图构建(dyna simultaneous localization and mapping,DynaSLAM)相比,定位精度基本持平,但在运行速度上实现了显著提升.
Dynamic feature point removal method with joint target detection and depth information
Aiming at the problem that the localization accuracy and robustness of visual localization systems are easily affected by dynamic feature points in dynamic environments,a dynamic feature point removal method combining target detection and depth information is proposed.The YOLOv7 target detection network is introduced to quickly obtain the target category and position information of the current image frame,and the coordinate attention(CA)mechanism is added to optimize the deep learning model and improve the target detection accuracy of the network.In addition,a dynamic feature point optimization strategy using depth information and pairwise geometric constraints is proposed.Dynamic feature points are effectively eliminated while as many static points as possible are retained,thus reducing the impact of dynamic points on the localization accuracy and robustness of the system.Experimental validation is performed on the publicly available dataset TUM.The results show that the proposed scheme has obvious advantages in terms of localization accuracy and robustness compared with ORB-SLAM2.At the same time,compared with DynaSLAM,the localization accuracy is basically the same,but the operation speed is significantly improved.
dynamic feature point eliminationtarget detectionvisual localizationdeep learningdy-namic scene
国家科技期刊平台
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